Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,354
2	New Jersey	18,116
3	Massachusetts	14,625
4	Rhode Island	14,150
5	District of Columbia	12,549
6	Connecticut	11,987
7	Delaware	9,863
8	Illinois	9,601
9	Maryland	8,937
10	Louisiana	8,701
11	Nebraska	7,415
12	Iowa	6,272
13	Pennsylvania	5,993
14	Michigan	5,851
15	South Dakota	5,690
16	Virginia	5,318
17	Indiana	5,300
18	Mississippi	5,292
19	Colorado	4,612
20	Minnesota	4,471
21	Georgia	4,309
22	Alabama	3,779
23	New Mexico	3,719
24	Kansas	3,467
25	North Dakota	3,448
26	New Hampshire	3,445
27	Tennessee	3,403
28	Wisconsin	3,186
29	Utah	3,127
30	Ohio	3,078
31	Washington	3,020
32	California	2,926
33	Nevada	2,824
34	North Carolina	2,800
35	Arizona	2,764
36	Florida	2,645
37	Arkansas	2,466
38	South Carolina	2,359
39	Texas	2,278
40	Kentucky	2,271
41	Missouri	2,203
42	Maine	1,747
43	Oklahoma	1,661
44	Idaho	1,627
45	Vermont	1,575
46	Wyoming	1,572
47	Puerto Rico	1,212
48	West Virginia	1,131
49	Oregon	1,019
50	Alaska	650
51	Montana	485
52	Hawaii	453

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Massachusetts	255
2	District of Columbia	150
3	Maryland	132
4	Delaware	126
5	Nebraska	119
6	Rhode Island	112
7	Virginia	111
8	Louisiana	110
9	Mississippi	107
10	Alabama	102
11	Illinois	101
12	Minnesota	99
13	Connecticut	91
14	North Carolina	83
15	Iowa	81
16	New Jersey	77
17	Utah	77
18	Arizona	75
19	Arkansas	73
20	California	72
21	South Carolina	65
22	Indiana	63
23	South Dakota	63
24	Michigan	61
25	Georgia	58
26	New York	58
27	Colorado	55
28	Tennessee	50
29	New Mexico	48
30	New Hampshire	47
31	Texas	46
32	North Dakota	45
33	Wisconsin	44
34	Pennsylvania	43
35	Ohio	40
36	Washington	39
37	Florida	36
38	Nevada	35
39	Kentucky	34
40	Kansas	31
41	Missouri	31
42	Maine	30
43	Puerto Rico	23
44	Idaho	19
45	Oklahoma	19
46	Alaska	18
47	Oregon	13
48	West Virginia	10
49	Wyoming	10
50	Montana	8
51	Vermont	4
52	Hawaii	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,530
2	New Jersey	1,319
3	Connecticut	1,113
4	Massachusetts	1,020
5	Rhode Island	679
6	District of Columbia	663
7	Louisiana	602
8	Michigan	553
9	Pennsylvania	435
10	Illinois	430
11	Maryland	422
12	Delaware	377
13	Indiana	318
14	Colorado	253
15	Mississippi	248
16	Georgia	193
17	Ohio	188
18	Minnesota	187
19	New Hampshire	180
20	Iowa	176
21	New Mexico	172
22	Virginia	163
23	Washington	148
24	Nevada	137
25	Alabama	131
26	Missouri	128
27	Arizona	126
28	Florida	114
29	California	108
30	Wisconsin	102
31	Kentucky	99
32	South Carolina	97
33	Nebraska	94
34	North Carolina	88
35	Vermont	88
36	Oklahoma	84
37	North Dakota	83
38	Kansas	74
39	South Dakota	70
40	Maine	66
41	Texas	58
42	Tennessee	52
43	Idaho	46
44	Arkansas	44
45	Puerto Rico	42
46	West Virginia	42
47	Oregon	36
48	Utah	35
49	Wyoming	29
50	Montana	15
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	15
2	Connecticut	9
3	Rhode Island	8
4	New Jersey	7
5	Delaware	4
6	Maryland	4
7	District of Columbia	3
8	Georgia	3
9	Illinois	3
10	Iowa	3
11	Michigan	3
12	Minnesota	3
13	Mississippi	3
14	New York	3
15	Alabama	2
16	Louisiana	2
17	Missouri	2
18	New Mexico	2
19	Ohio	2
20	Pennsylvania	2
21	Arizona	1
22	California	1
23	Colorado	1
24	Indiana	1
25	Nebraska	1
26	New Hampshire	1
27	North Carolina	1
28	South Carolina	1
29	South Dakota	1
30	Virginia	1
31	Wisconsin	1
32	Wyoming	1
33	Alaska	0
34	Arkansas	0
35	Florida	0
36	Hawaii	0
37	Idaho	0
38	Kansas	0
39	Kentucky	0
40	Maine	0
41	Montana	0
42	Nevada	0
43	North Dakota	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Tennessee	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	Washington	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,361	1	99
Dakota	Nebraska	83,941	2	99
Lincoln	Arkansas	75,476	3	99
Nobles	Minnesota	71,386	4	99
Lake	Tennessee	59,008	5	99
Davidson	Tennessee	7,948	257	91
Richland	South Carolina	3,680	700	77
Pierce	Washington	2,167	1090	65
Orange	California	2,057	1136	63
York	South Carolina	1,484	1423	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Terrell	Georgia	3,048	1	99
Early	Georgia	3,042	2	99
Hancock	Georgia	2,956	3	99
Randolph	Georgia	2,803	4	99
Essex	New Jersey	2,086	5	99
Richland	South Carolina	159	612	80
Davidson	Tennessee	91	852	72
Pierce	Washington	87	874	72
Orange	California	46	1223	61
York	South Carolina	28	1427	54

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.